Apache Spark vs Serverless: What are the differences?
Introduction:
Apache Spark and Serverless are two popular technologies used for big data processing and analytics. Although they both provide solutions for handling large datasets, there are key differences between them. In this article, we will discuss the six main differences between Apache Spark and Serverless.
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Deployment Model: Apache Spark is typically deployed on a cluster of machines, where data is distributed and processing is done in parallel. On the other hand, Serverless technologies like AWS Lambda or Azure Functions are event-driven and allow developers to run code on demand without having to manage the underlying infrastructure.
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Resource Allocation: In Apache Spark, resources need to be pre-allocated and managed manually, specifying how much memory or cores should be allocated for each job. Serverless platforms, on the other hand, automatically allocate resources based on the demand, scaling up or down as needed. This allows for better resource utilization and cost optimization.
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Scalability: Apache Spark provides horizontal scalability, meaning it can scale by adding more machines to the cluster. Serverless platforms also provide scalability, but at the function level. Each function can scale independently based on the incoming workload, without affecting other functions.
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State Management: Apache Spark provides an in-memory computing model, allowing users to persist data in memory for faster processing. Serverless platforms, on the other hand, are stateless by design. They are designed to handle short-lived functions that process small units of data and do not provide built-in support for persistent state.
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Cost Model: Apache Spark requires the setup and management of a dedicated cluster, which may require upfront costs for hardware and infrastructure. Serverless platforms follow a pay-as-you-go pricing model, where users only pay for the actual execution time and resources used by their functions, leading to potential cost savings, especially for sporadic workloads.
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Flexibility: Apache Spark provides a wide range of data processing and analysis capabilities through its extensive library ecosystem. It supports batch processing, interactive queries, machine learning, and graph processing. Serverless platforms, on the other hand, are more focused on event-driven functions and are optimized for short-lived, stateless operations.
In Summary, Apache Spark and Serverless differ in their deployment model, resource allocation, scalability, state management, cost model, and flexibility. Apache Spark requires a dedicated cluster, manual resource allocation, and is capable of handling large and complex workloads. Serverless platforms are event-driven, automatically allocate resources, and are optimized for short-lived functions with lower upfront costs.
